Systems and methods herein generally relate to call center operation and, in particular, to predicting the class of future customer calls to a call center.
Call centers typically deal with huge volumes of incoming phone calls. Currently, when a customer calls a call center, the agent who answers the call has no idea on the subject of the incoming call. With no prior information on the upcoming conversation, other than a general topic out of a few that an Interactive Voice Response Unit (IVR) may suggest, each agent needs to properly interpret the problem formulation described by the customer. This process takes time, including when it is a recurring issue or when the customer is calling for a follow-up on an existing issue. Moreover, misunderstandings often slow down the process.
Enabling call center agents to anticipate future incoming calls would result in faster and more efficient interactions. Predicting the precise class/type of a future call for a given customer would be immensely helpful for seamless interactions, better customer satisfaction, and increased agent productivity. Further, pragmatic staffing strategies could be put in place, as well as a better call assignment according to agents, based on performance on call types. A sound prediction model would need to leverage agent operational workflows while dealing with very noisy data stored in call center databases and accounting for existing information as training features.